جستجوی مقالات مرتبط با کلیدواژه « Radon Transform » در نشریات گروه « زمین شناسی »

The reflection waves, which is reflected between the subsurface or free surface reflectors more than once before being received on the receivers, are called multiple reflections. Multiple reflections, often destructively interact with the primary reflections and reduce the quality of the seismic image. An inverse filter based on predictive deconvolution using the periodic feature is used to attenuate multiple reflections in the water. Multiple and primary reflections show different moveout and travel-times, This property is the basis of the theory of many multiple attenuation techniques such as CMP stacking, F-K filter, and Radon transform. Radon transform was first introduced by Johann Radon (1917) and for the first time, parabolic Radon conversion was used as a multiple attenuation technique by Hampson (1986). Since then, the Radon transform became one of the most widely used tools to suppress multiple noises. Goudarzi and Riahi (2013) presented WDGA method based on the data type, as an efficient way of attenuating various seismic noises. However, this approach, if there is a high level of random noise in the data, cannot well separate the coherent noise from the reflections. Here we try to introduce a new method to solve this problem. 

Methodology and Approaches:

The proposed method in this research is called wavelet domain noise analysis (WDNA) algorithm. Similar to WDGA, this method is based on data, but because of the use of the split Bergman iteration is less sensitive to random noise. It also reduces the time to reach an optimal solution and it has better convergence. These features enable better detection of the desired noise and better signal separation from the noise. The goal of this research is to apply the benefits of Radon transform, and at the same time, to use the DT-RADWT wavelet transform capabilities to provide high resolution. We take advantage of the split Bergman iterative algorithm to build a full multiple reflection model from initial multiple models (achieved from Radon filter). Finally, in the DT-RADWT domine, full model of multiple reflections would be subtracted from the input data, and thus, the filtered data would be obtained.

In this research, the WDNA algorithm has been introduced and its application in attenuating multiple reflections from seismic data has been investigated. The WDNA algorithm is based on the data and requires an initial noise model that is obtained from Radon transform (or any other suitable filter) to attenuate multiple reflections and in the dual-tree wavelet transform domain, it is used to produce a complete noise model with the Bergman iteration algorithm. Subtracting the full noise model from seismic input data yields almost no multiple reflection noise and the initial reflections are well maintained. The use of the DT-RADWT wavelet transform increases the frequency resolution and split Bergman algorithm helps to achieve a fast convergent solution that also causes insensitivity with random noise in the attenuation process of multiple reflections. The results of applying the WDNA method on synthetic and real data have resulted in better outputs than Radon and WDGA.

Summary Radon transform is a useful tool in seismic data processing with lots of applications. However, incomplete information decreases its resolution and limits its applicability. Sparseness is a valid criterion to overcome this problem. In this paper, a forward-backward splitting algorithm is used to solve an l1-norm regularized Radon transform in order to obtain a high resolution linear Radon transform (LRT). Then, it is applied on down-up going wavefield separation in vertical seismic profiling (VSP) data and also seismic interference (SI) noise attenuation. It is also shown that a sparse LRT can be used to enhance the quality of teleseismic data.
Introduction Overlapping wavefields in time-space domain can be separated in Radon domain, however, different reasons cause resolution problems and aliasing in Radon transform. Obtaining a sparse Radon panel enables us to separate coefficients of each event without harming the others. Using sparsity promoting methods results in a high resolution Radon domain with minimum non-zero elements. An l2-l1 norm cost function is constructed and a forward-backward splitting algorithm is employed to obtain a sparse model, which can be used for different purposes among which VSP wavefield separation, SI noise attenuation and wavefield enhancement are dealt with here. Both down- and upgoing wavefields of VSP data contains information of subsurface but they need to be separated. Mapping their coefficients to separable regions using a sparse linear Radon transform is an efficient solution. One other application could be SI noise attenuation. This kind of marine noise is originated by other surveys in the same area and overpowers reflections. According to their linear characteristics and the use of sparse LRT, they can be subtracted from the data. Moreover, it is shown that injecting sparsity to Radon domain eliminates random noise and also non-linear events. Furthermore, low quality traces can be replaced by interpolated new reconstructed traces applying a mask on misfit term in the cost function. This can be used to enhance the quality of teleseismic data with linear wavefields.
Methodology and Approaches The ability of separating coefficients in Radon domain relies on the resolution of Radon transform. Least square regularization results in a smooth solution, which causes the application limitation. Here, a sparse LRT is developed by applying an l1-norm constraint on Radon model. This transformation can be applied on VSP data to map upgoing and downgoing wavefields to negative and positive slowness regions, respectively. Then, each of them can be reconstructed separately using inverse Radon operator. High amplitude seismic interference noise is harmful to many processing steps and they need to be attenuated beforehand. Their linear coherency in shot gathers makes LRT an appropriate tool to eliminate them. Using a sparse LRT converges SI plane waves energy, however, attenuates reflections coefficients. Thus, only SI noise can be reconstructed, and then, subtracted from marine shot gathers. Sparse data acquisition in teleseismic data, and also, the presence of random noise reduce the quality of the data and make arrival time picking difficult. Sparsity in Radon panel and interpolation increases the resolution of the reconstructed data so that the P- and S-wave arrival times can be determined with more accuracy.
Results and Conclusions Synthetic and real numerical examples demonstrated the efficiency of applying a forward-backward splitting algorithm to solve an l2-l1 cost function for obtaining a sparse Radon panel in which even linear wavefields with very close slopes can be identified. This method is a feasible and effective way to separate overlapping upgoing and downgoing VSP wavefields, and to attenuate the seismic interference noise and to enhance teleseismic wavefield.